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Clustering and automatic labelling within time series of categorical observations—with an application to marine log messages

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  • Emanuele Gramuglia
  • Geir Storvik
  • Morten Stakkeland

Abstract

System logs or log files containing textual messages with associated time stamps are generated by many technologies and systems. The clustering technique proposed in this paper provides a tool to discover and identify patterns or macrolevel events in this data. The motivating application is logs generated by frequency converters in the propulsion system on a ship, while the general setting is fault identification and classification in complex industrial systems. The paper introduces an offline approach for dividing a time series of log messages into a series of discrete segments of random lengths. These segments are clustered into a limited set of states. A state is assumed to correspond to a specific operation or condition of the system, and can be a fault mode or a normal operation. Each of the states can be associated with a specific, limited set of messages, where messages appear in a random or semi‐structured order within the segments. These structures are in general not defined a priori. We propose a Bayesian hierarchical model where the states are characterised both by the temporal frequency and the type of messages within each segment. An algorithm for inference based on reversible jump MCMC is proposed. The performance of the method is assessed by both simulations and operational data.

Suggested Citation

  • Emanuele Gramuglia & Geir Storvik & Morten Stakkeland, 2021. "Clustering and automatic labelling within time series of categorical observations—with an application to marine log messages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(3), pages 714-732, June.
    • Handle: RePEc:bla:jorssc:v:70:y:2021:i:3:p:714-732
    DOI: 10.1111/rssc.12483
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    References listed on IDEAS

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    1. Papastamoulis, Panagiotis, 2016. "label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(c01).
    2. Vanessa Didelez, 2008. "Graphical models for marked point processes based on local independence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 245-264, February.
    3. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
    4. repec:dau:papers:123456789/6069 is not listed on IDEAS
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